Greene Anna C, Giffin Kristine A, Greene Casey S, Moore Jason H
Brief Bioinform. 2016 Jan;17(1):43-50. doi: 10.1093/bib/bbv018. Epub 2015 Mar 30.
Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs.
现代技术能够生成海量数据，用以测量复杂的生物系统。计算生物学家和生物信息学科学家越来越多地被要求利用这些数据来揭示关键的系统层面特性。我们回顾了在大数据时代课程设置的变化程度。我们确定了处理大数据的科学家在各个领域应具备的关键能力，并利用这些信息来建议开设课程以满足这些不断增长的需求。虽然传统上生物信息学专业会培养学生进行数据密集型科学研究，但我们确定了在这个时代特别重要的生物学、计算和统计学重点领域，这些领域可以纳入现有的课程体系。对于每个领域，我们建议开设一门围绕这些主题构建的课程，该课程可以全部或部分地改编到现有的课程中。总之，与大数据相关的特定挑战为更新现有课程提供了一个重要机会，但我们预计生物信息学培训项目不会进行全面重新设计。
